The variety and volume of data that is being transmitted via the internet today, as well as the velocity at which it is being transmitted is constantly increasing. These data sets are so voluminous that traditional data processing software just can’t manage them, and so they have come to be called “Big Data”. The massive volume of data necessary to address new and more complex business operations has brought about new challenges to meeting the requirements of these new developments.
What is the Definition of Big Data?
Big data is a combination of unstructured, semi-structured, and structured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.
Big data is often characterized by the three V's:
- the large volume of data found in many environments;
- the wide variety of data types frequently stored in big data systems; and
- the velocity at which much of the data is generated, collected and processed.
More recently, several other V's have been added to different descriptions of big data, including veracity, value and variability. Although big data does not equate to any specific volume of data, big data deployments often involve terabytes, petabytes, and even exabytes of data created and collected over time.
Why Big Data Matters?
Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns, and take other actions that, ultimately, can increase revenue and profits. Businesses that use it effectively hold a potential competitive advantage over those that don't because they're able to make faster and more informed business decisions.
Here are some more examples of how big data is used by organizations:
- In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids.
- Financial services firms use big data systems for risk management and real-time analysis of market data.
- Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes.
- Other government uses include emergency response, crime prevention and smart city initiatives.
Types of Big Data
Following are the types of Big Data:
Unstructured
Any data with unknown form or structure is called unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Now a days, organizations have a wealth of data in their possession but unfortunately, because this data is in its raw form or an unstructured format, they don’t know how to derive value out of it.
Semi-structured
Semi-structured data can contain both unstructured and structured data. We can see semi-structured data as structured in form but not actually defined. An example of semi-structured data is data represented in Extensible Markup Language (XML) files.
Structured
Any data that can be stored, accessed, and processed in the form of static and unalterable data, such as “fixed format data” is termed “structured” data. Computer processing capabilities have grown to include techniques for working with such kinds of data, where the format is well known and pre-set in advance. Nowadays, the size of such data has grown to where typical sizes are in the rage of multiple zettabytes.
How Big Data Works?
Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:
- Integrate
Big data brings together data from many different sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale. During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.
- Manage
Big data requires storage and your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.
- Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.
Summary
- Big Data: Big Data means data that is huge in size. It is a term used to describe a collection of data that is huge in size and may be growing exponentially with time.
- Big Data could be: Unstructured, semi-structured, or structured.
- Big Data characteristics: Volume, variety, velocity.
